Detecção de defeitos em tecidos através de redes neurais convolucionais - Souza, Lucas Candiani (2022)
This work aims to apply transfer learning to a detector based on convolutional neural networks to identify defects and patterns through tissue images. With Industry 4.0, intelligent systems are increasingly being applied in an integrated way in production; therefore, the objective of this work is to develop a model to perform the automatic identification of fabric patterns and defects, since an effective detector applied in a production line can reduce costs and provide data on production in general. The dataset used was the ZJU-Leaper, the backbone used was from RetinanetR101 and for code execution several Python libraries were used, such as Pytorch, OpenCV and numpy, in addition to the Detectron2 API. The mAP (mean average precision) of the model developed, calculated for all classes (6 patterns and “defect”) in the validation set, is 92.4%.